Controlling interactive fashion based on facial expressions

ABSTRACT

Methods and systems are disclosed for performing operations comprising: receiving an image that includes a depiction of a person wearing a fashion item; generating a segmentation of the fashion item worn by the person depicted in the image; identifying a facial expression of the user depicted in the image; and in response to identifying the facial expression, applying one or more augmented reality elements to the fashion item worn by the person based on the segmentation of the fashion item worn by the person.

TECHNICAL FIELD

The present disclosure relates generally to providing augmented realityexperiences using a messaging application.

BACKGROUND

Augmented-Reality (AR) is a modification of a virtual environment. Forexample, in Virtual Reality (VR), a user is completely immersed in avirtual world, whereas in AR, the user is immersed in a world wherevirtual objects are combined or superimposed on the real world. An ARsystem aims to generate and present virtual objects that interactrealistically with a real-world environment and with each other.Examples of AR applications can include single or multiple player videogames, instant messaging systems, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some examples.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a block diagram showing an example AR fashion control system,according to example examples.

FIGS. 6, 7A, 7B, 8, and 9 are diagrammatic representations of outputs ofthe AR fashion control system, in accordance with some examples.

FIG. 10 is a flowchart illustrating example operations of the AR fashioncontrol system, according to some examples.

FIG. 11 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 12 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examples.It will be evident, however, to those skilled in the art, that examplesmay be practiced without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

Typically, virtual reality (VR) and augmented reality (AR) systemsdisplay images representing a given user by capturing an image of theuser and, in addition, obtaining a depth map using a depth sensor of thereal-world human body depicted in the image. By processing the depth mapand the image together, the VR and AR systems can detect positioning ofa user in the image and can appropriately modify the user or backgroundin the images. While such systems work well, the need for a depth sensorlimits the scope of their applications. This is because adding depthsensors to user devices for the purpose of modifying images increasesthe overall cost and complexity of the devices, making them lessattractive.

Certain systems do away with the need to use depth sensors to modifyimages. For example, certain systems allow users to replace a backgroundin a videoconference in which a face of the user is detected.Specifically, such systems can use specialized techniques that areoptimized for recognizing a face of a user to identify the background inthe images that depict the user’s face. These systems can then replaceonly those pixels that depict the background so that the real-worldbackground is replaced with an alternate background in the images. Suchsystems though are generally incapable of recognizing a whole body of auser. As such, if the user is more than a threshold distance from thecamera such that more than just the face of the user is captured by thecamera, the replacement of the background with an alternate backgroundbegins to fail. In such cases, the image quality is severely impacted,and portions of the face and body of the user can be inadvertentlyremoved by the system as the system falsely identifies such portions asbelonging to the background rather than the foreground of the images.Also, such systems fail to properly replace the background when morethan one user is depicted in the image or video feed. Because suchsystems are generally incapable of distinguishing a whole body of a userin an image from a background, these systems are also unable to applyvisual effects to certain portions of a user’s body, such as articles ofclothing.

The disclosed techniques improve the efficiency of using the electronicdevice by segmenting articles of clothing or garments worn by a userdepicted in an image or video, such as a shirt worn by the user depictedin the image in addition to segmenting the whole body of the userdepicted in the image or video. By segmenting the articles of clothingor garments worn by a user or worn by different respective usersdepicted in an image and segmenting the whole body of the user, thedisclosed techniques can apply one or more visual effects to the imageor video based on gestures performed by the user in the image or video.Particularly, the disclosed techniques can apply one or more augmentedreality elements to a shirt depicted in the image or video and thenmodify the one or more augmented reality elements based on a facialexpression of the user in the image or video.

In an example, the disclosed techniques apply a machine learningtechnique to generate a segmentation of a shirt worn by a user depictedin an image (e.g., to distinguish pixels corresponding to the shirt ormultiple garments worn by the user from pixels corresponding to abackground of the image or a user’s body parts). In this way, thedisclosed techniques can apply one or more visual effects (e.g., basedon identified facial expressions) to the shirt worn by a user that hasbeen segmented in the current image. Also, by generating thesegmentation of the shirt, a position/location of the shirt in a videofeed can be tracked independently or separately from positions of auser’s body parts, such as a hand. This enables the disclosed techniquesto detect activation of or selections associated with augmented realityelements displayed on the shirt worn by the user based on a location ofthe user’s hand in the video feed. For example, a user’s hand can bedetected as being positioned over a given augmented reality elementdisplayed on the shirt and, in response, the corresponding optionrepresented by the given augmented reality element can be activated orselected.

As a result, a realistic display is provided that shows the user wearinga shirt while also presenting augmented reality elements on the shirt ina way that is intuitive for the user to interact with and select. Asused herein, “article of clothing” and “garment” are usedinterchangeably and should be understood to have the same meaning. Thisimproves the overall experience of the user in using the electronicdevice. Also, by performing such segmentations without using a depthsensor, the overall amount of system resources needed to accomplish atask is reduced.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108 and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications, such as external apps 109 using Application ProgrammingInterfaces (APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Programming Interface (API) server 116 is coupled to, andprovides a programmatic interface to, application servers 114. Theapplication servers 114 are communicatively coupled to a database server120, which facilitates access to a database 126 that stores dataassociated with messages processed by the application servers 114.Similarly, a web server 128 is coupled to the application servers 114,and provides web-based interfaces to the application servers 114. Tothis end, the web server 128 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The API server 116 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationservers 114. Specifically, the API server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The API server 116 exposes various functionssupported by the application servers 114, including accountregistration, login functionality, the sending of messages, via theapplication servers 114, from a particular messaging client 104 toanother messaging client 104, the sending of media files (e.g., imagesor video) from a messaging client 104 to a messaging server 118, and forpossible access by another messaging client 104, the settings of acollection of media data (e.g., story), the retrieval of a list offriends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including for example a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208 (shown in FIG. 2 ). Scan functionalityincludes activating and providing one or more augmented realityexperiences on a client device 102 when an image is captured by theclient device 102. Specifically, the messaging client 104 on the clientdevice 102 can be used to activate a camera. The camera displays one ormore real-time images or a video to a user along with one or more iconsor identifiers of one or more augmented reality experiences. The usercan select a given one of the identifiers to launch the correspondingaugmented reality experience or perform a desired image modification(e.g., replacing a garment being worn by a user in a video or recoloringthe garment worn by the user in the video or modifying the garment basedon a gesture performed by the user).

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3 ) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102 (e.g., a “native app”), or a small-scale version of thethird-party application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the third-party applicationincludes a subset of features and functions of the third-partyapplication (e.g., the full-scale, native version of the third-partystandalone application) and is implemented using a markup-languagedocument. In one example, the small-scale version of the third-partyapplication (e.g., an “applet”) is a web-based, markup-language versionof the third-party application and is embedded in the messaging client104. In addition to using markup-language documents (e.g., a .*ml file),an applet may incorporate a scripting language (e.g., a .*js file or a.json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from a external app(s) server 110, a markup-language documentassociated with the small-scale external application and processing sucha document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client 104, with the ability to share an item, status, state,or location in an external resource with one or more members of a groupof users into a chat session. The shared item may be an interactive chatcard with which members of the chat can interact, for example, to launchthe corresponding external resource, view specific information withinthe external resource, or take the member of the chat to a specificlocation or state within the external resource. Within a given externalresource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media itemsin the responses, based on a current context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external application 109 (or applets)can vary based on how the menu is launched by the user (e.g., from aconversation interface or from a non-conversation interface).

The messaging client 104 can present to a user one or more ARexperiences that can be controlled based on facial expressions of aperson depicted in the image. As an example, the messaging client 104can detect a face of a person in an image or video captured by theclient device 102. The messaging client 104 can segment an article ofclothing (or fashion item), such as a shirt, in the image or video.While the disclosed examples are discussed in relation to a shirt wornby a person (or user of the client device 102) depicted in an image orvideo, similar techniques can be applied to any other article ofclothing or fashion item, such as a dress, pants, glasses, jewelry, ahat, ear muffs, and so forth.

In response to segmenting the shirt, the messaging client 104 can trackthe 2D/3D position of the shirt in the video separately from theposition of the body of the person or user. This enables the messagingclient 104 to present one or more AR graphical elements on the shirt andallows the messaging client 104 to modify the AR graphical elementsbased on facial expressions performed by the person detected by trackingmovement of a body part of the user in relation to the segmented shirt.

As one example, the messaging client 104 can generate text for displayon the shirt worn by the person depicted in the image or video. The textcan be rendered on the shirt worn by the person in response to detectinga particular facial expression, or words of existing text can bereplaced with other words based on changes to facial expressions of theperson depicted in the image or video. For example, the messaging client104 can detect a face of the person depicted in the image or video. Inresponse to detecting the face, the messaging client 104 can apply oneor more machine learning techniques to features of the face to detect afacial expression of the face (e.g., to detect that the facialexpression is associated with a particular emotion or mood, such ashappy, sad, surprised, confused, upset, and so forth). The messagingclient 104 can search a database of words to identify one or more wordsthat are associated with the facial expression (e.g., the emotion ormood of the facial expression). The messaging client 104 can select asubset of the identified words, such as based on a rank, popularity,user profile, randomness, uniqueness, or any combination thereof. Themessaging client 104 can then add an augmented reality element thatincludes text to the shirt worn by the person in the image or video. Themessaging client 104 can include, in the text of the augmented realityelement, the selected subset of the identified words.

In some implementations, the messaging client 104 can detect a phrasewritten physically on the shirt worn by the person depicted in the imageor video. The messaging client 104 can perform word recognition to thephrase to identify a word associated with an adjective that describes anemotion or mood. In response to identifying the word associated with theadjective, the messaging client 104 can generate an augmented realityversion of the identified word and include text in the augmented realityversion that represents the facial expression. The messaging client 104can replace the physical word with the augmented reality version of theword, such as by overlaying the augmented reality version of the word ontop of the physical word that is on the shirt worn by the persondepicted in the image or video. In this way, the messaging client 104can generate new text or modify existing text on the shirt worn by theperson depicted in the image or video to represent a facial expressionof the person depicted in the image or video. As the facial expressionchanges in real time, the messaging client 104 can update the augmentedreality words displayed on the shirt worn by the person depicted in theimage or video to represent different facial expressions.

As one example, the messaging client 104 can generate an image or videofor display on the shirt worn by the person depicted in the image orvideo. The image or video can be rendered on the shirt worn by theperson in response to detecting a particular facial expression, or animage or video that is playing or displayed in the background can beadjusted based on changes to facial expressions of the person depictedin the image or video. For example, the messaging client 104 can detecta face of the person depicted in the image or video. In response todetecting the face, the messaging client 104 can apply one or moremachine learning techniques to features of the face to detect a facialexpression of the face (e.g., to detect that the facial expression isassociated with a particular emotion or mood, such as happy, sad,surprised, confused, upset, and so forth). The messaging client 104 cansearch a database of words to identify one or more words that areassociated with the facial expression (e.g., the emotion or mood of thefacial expression). The messaging client 104 can select a subset of theidentified words, such as based on a rank, popularity, user profile,randomness, uniqueness, or any combination thereof. The messaging client104 can then search for an image or video (e.g., an avatar or emoji)associated with the selected subset of the identified words and add theimage or video to the shirt worn by the person in the image or video.

For example, the messaging client 104 can detect that the facialexpression is associated with a happy emotion. In response, themessaging client 104 can search for videos associated with a happymetadata tag and that have a popularity value that exceeds a popularitythreshold. The messaging client 104 can then select a random one of thevideos and display the selected video on the shirt worn by the persondepicted in the image or video. As another example, the messaging client104 can detect that the facial expression is associated with a sademotion. In response, the messaging client 104 can search for imagesassociated with a sad metadata tag and that have a popularity value thatexceeds a popularity threshold. The messaging client 104 can then selecta random one of the images and display the selected image on the shirtworn by the person depicted in the image or video. As another example,the messaging client 104 can detect a media item (e.g., an image orvideo) that is being displayed or played in the background of the imageor video that depicts the person. The messaging client 104 can search adatabase of media item parameter adjustments to identify a media itemparameter adjustment associated with the facial expression of the persondepicted in the image or video. The messaging client 104, in response toidentifying the media item parameter adjustment, can adjust the mediaitem (e.g., the aspect ratio, the brightness, pause, play, fast-forward,rewind) based on the identified media item parameter. Specifically, ifthe messaging client 104 determines that the facial expression is upset,the messaging client 104 can identify a skip chapter media itemparameter adjustment associated with an upset emotion. The messagingclient 104 can apply that skip chapter parameter adjustment to a videobeing played in the background of the image or video that depicts theperson wearing the shirt.

As another example, the messaging client 104 can adjust an expression ofan augmented reality avatar or emoji depicted on a shirt worn by aperson in the image or video to match the facial expression of theperson depicted in the image or video. For example, the messaging client104 can detect a face of the person depicted in the image or video. Inresponse to detecting the face, the messaging client 104 can apply oneor more machine learning techniques to features of the face to detect afacial expression of the face (e.g., to detect that the facialexpression is associated with a particular emotion or mood, such ashappy, sad, surprised, confused, upset, and so forth). The messagingclient 104 can search a database of avatar or emoji expressions that areassociated with the facial expression (e.g., the emotion or mood of thefacial expression). The messaging client 104 can then adjust a facialexpression of an avatar or emoji displayed on the shirt worn by theperson depicted in the image or video to match or correspond to thefacial expression of the person depicted in the image or video.

As one example, the messaging client 104 can adjust a fashion itempattern, type, or style based on makeup applied to a face of a persondepicted in an image or video. For example, the messaging client 104 canprocess one or more facial features of a person depicted in an image orvideo. The messaging client 104 can determine a style of the makeup onthe face of the person. The makeup can be physically applied to the faceof the person depicted in the image or can be applied in augmentedreality using an augmented reality makeup try-on experience. In animplementation, the messaging client 104 applies a machine learningtechnique to the makeup features or the face of the person depicted inthe image or video to estimate a classification of the makeup on theface of the person. Once the makeup classification is estimated, acorresponding style of the makeup is determined. The messaging client104 can search a database of fashion item patterns, types, or styles toidentify a fashion item pattern, type or style associated with the styleof makeup. The messaging client 104 can then select a given one of thefashion item patterns, styles, or types to generate an augmented realitygraphical element associated with the selected fashion item pattern,style, or type. The messaging client 104 applies the augmented realitygraphical element to the fashion item worn by the person depicted in theimage or video.

For example, the messaging client 104 can determine that the style ofthe makeup is associated with a particular animal (e.g., a lion orzebra). In such cases, the messaging client 104 obtains a characteristicof the animal (e.g., zebra or lion stripes) and applies augmentedreality zebra or lion stripes to a shirt worn by the person depicted inthe image or video. In an implementation, the messaging client 104 addsan augmented reality headwear to the person’s head (e.g., to representhorns) of the animal. As another example, the messaging client 104 candetermine that the makeup type is associated with a goth style and, inresponse, the messaging client 104 modifies a color, pattern, or type ofclothing worn by the person. The modification can be performed byoccluding or overlaying a portion of the physical clothing worn by theperson with augmented reality clothing.

As another example, the messaging client 104 can display, on the shirtdepicted in the image or video, an augmented reality version of anaugmented reality facial effect applied to the face of the persondepicted in the image or video. Specifically, the messaging client 104can detect an augmented reality facial effect applied to the face of theperson depicted in the image (e.g., such as augmented reality makeup).The messaging client 104 can, in response, generate an augmented realityversion of the augmented reality facial effect by copying a style,pattern, pose, outline, and color of the face of the person to which theaugmented reality facial effect has been applied into an augmentedreality version. The messaging client 104 can then overlay the augmentedreality version of the facial effect on top of a specified portion ofthe shirt worn by the person depicted in the image or video. In somecases, the messaging client 104 can present a purchase option togetherwith the depiction of the shirt and augmented reality version of thefacial effect on top of the shirt. In response to receiving a userselection of the purchase option, the messaging client 104 can transmitan image of the shirt with the augmented reality version of the facialeffect to a vendor that manufactures shirts. The messaging client 104can instruct the vendor to create a physical shirt in a size associatedwith the person that resembles the shirt worn by the user augmented withthe augmented reality version of the facial effect. The manufacturedshirt is then shipped to the home of the person.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 further includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or image that canbe overlaid on top of a photograph taken by the client device 102. Inanother example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to obtain augmented reality experiencesand presents identifiers of such experiences in one or more userinterfaces (e.g., as icons over a real-time image or video or asthumbnails or icons in interfaces dedicated for presented identifiers ofaugmented reality experiences). Once an augmented reality experience isselected, one or more images, videos, or augmented reality graphicalelements are retrieved and presented as an overlay on top of the imagesor video captured by the client device 102. In some cases, the camera isswitched to a front-facing view (e.g., the front-facing camera of theclient device 102 is activated in response to activation of a particularaugmented reality experience) and the images from the front-facingcamera of the client device 102 start being displayed on the clientdevice 102 instead of the rear-facing camera of the client device 102.The one or more images, videos, or augmented reality graphical elementsare retrieved and presented as an overlay on top of the images that arecaptured and displayed by the front-facing camera of the client device102.

In other examples, the augmentation system 208 is able to communicateand exchange data with another augmentation system 208 on another clientdevice 102 and with the server via the network 112. The data exchangedcan include a session identifier that identifies the shared AR session,a transformation between a first client device 102 and a second clientdevice 102 (e.g., a plurality of client devices 102 include the firstand second devices) that is used to align the shared AR session to acommon point of origin, a common coordinate frame, functions (e.g.,commands to invoke functions) as well as other payload data (e.g., text,audio, video or other multimedia data).

The augmentation system 208 sends the transformation to the secondclient device 102 so that the second client device 102 can adjust the ARcoordinate system based on the transformation. In this way, the firstand second client devices 102 synch up their coordinate systems andframes for displaying content in the AR session. Specifically, theaugmentation system 208 computes the point of origin of the secondclient device 102 in the coordinate system of the first client device102. The augmentation system 208 can then determine an offset in thecoordinate system of the second client device 102 based on the positionof the point of origin from the perspective of the second client device102 in the coordinate system of the second client device 102. Thisoffset is used to generate the transformation so that the second clientdevice 102 generates AR content according to a common coordinate systemor frame as the first client device 102.

The augmentation system 208 can communicate with the client device 102to establish individual or shared AR sessions. The augmentation system208 can also be coupled to the messaging server 118 to establish anelectronic group communication session (e.g., group chat, instantmessaging) for the client devices 102 in a shared AR session. Theelectronic group communication session can be associated with a sessionidentifier provided by the client devices 102 to gain access to theelectronic group communication session and to the shared AR session. Inone example, the client devices 102 first gain access to the electronicgroup communication session and then obtain the session identifier inthe electronic group communication session that allows the clientdevices 102 to access the shared AR session. In some examples, theclient devices 102 are able to access the shared AR session without aidor communication with the augmentation system 208 in the applicationservers 114.

The map system 210 provides various geographic location functions, andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104, and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game by issuing invitations to such other users from themessaging client 104. The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes ApplicationProgramming Interfaces (APIs) with functions that can be called orinvoked by the web-based application. In certain examples, the messagingserver 118 includes a JavaScript library that provides a giventhird-party resource access to certain user data of the messaging client104. HTML5 is used as an example technology for programming games, butapplications and resources programmed based on other technologies can beused.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets and the messaging client 104). This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a WebViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between an external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a GUI ofthe messaging client 104 to access features of the web-based externalresource, the messaging client 104 obtains the HTML5 file andinstantiates the resources necessary to access the features of theweb-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., onlytwo-dimensional avatars of users with or without different avatarcharacteristics). As another example, external resources that includesmall-scale versions of external applications (e.g., web-based versionsof third-party applications) are provided with access to a second typeof user data (e.g., payment information, two-dimensional avatars ofusers, three-dimensional avatars of users, and avatars with variousavatar characteristics). Avatar characteristics include different waysto customize a look and feel of an avatar, such as different poses,facial features, clothing, and so forth.

The AR fashion control system 224 segments a fashion item, such as ashirt, worn by a user depicted in an image (or video) or multiplefashion items worn respectively by multiple users depicted in an image(or video). An illustrative implementation of the AR fashion controlsystem 224 is shown and described in connection with FIG. 5 below.

Specifically, the AR fashion control system 224 is a component that canbe accessed by an AR/VR application implemented on the client device102. The AR/VR application uses an RGB camera to capture a monocularimage of a user and the garment or garments (alternatively referred toas fashion item(s)) worn by the user. The AR/VR application appliesvarious trained machine learning techniques on the captured image of theuser wearing the garment to segment the garment (e.g., a shirt, jacket,pants, dress, and so forth) worn by the user in the image and to applyone or more AR visual effects to the captured image. Segmenting thegarment results in an outline of the borders of the garment that appearin the image or video. Pixels within the borders of the segmentedgarment correspond to the garment or clothing worn by the user. Thesegmented garment is used to distinguish the clothing or garment worn bythe user from other objects or elements depicted in the image, such asparts of the user’s body (e.g., arms, head, legs, and so forth) and thebackground of the image which can be separately segmented and tracked.In some implementations, the AR/VR application continuously capturesimages of the user wearing the garment in real time or periodically tocontinuously or periodically update the applied one or more visualeffects. This allows the user to move around in the real world and seethe one or more visual effects update in real time.

In order for the AR/VR application to apply the one or more visualeffects directly from a captured RGB image, the AR/VR applicationobtains a trained machine learning technique from the AR fashion controlsystem 224. The trained machine learning technique processes thecaptured RGB image to generate a segmentation from the captured imagethat corresponds to the garment worn by the user(s) depicted in thecaptured RGB image.

In training, the AR fashion control system 224 obtains a first pluralityof input training images that include depictions of one or more userswearing different garments. These training images also provide theground truth information about the segmentations of the garments worn bythe users depicted in each image. A machine learning technique (e.g., adeep neural network) is trained based on features of the plurality oftraining images. Specifically, the first machine learning techniqueextracts one or more features from a given training image and estimatesa segmentation of the garment worn by the user depicted in the giventraining image. The machine learning technique obtains the ground truthinformation corresponding to the training image and adjusts or updatesone or more coefficients or parameters to improve subsequent estimationsof segmentations of the garment, such as the shirt.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, are described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

The database 126 can also store data pertaining to individual and sharedAR sessions. This data can include data communicated between an ARsession client controller of a first client device 102 and another ARsession client controller of a second client device 102, and datacommunicated between the AR session client controller and theaugmentation system 208. Data can include data used to establish thecommon coordinate frame of the shared AR scene, the transformationbetween the devices, the session identifier, images depicting a body,skeletal joint positions, wrist joint positions, feet, and so forth.

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes augmented reality content items (e.g., corresponding toapplying augmented reality experiences). An augmented reality contentitem or augmented reality item may be a real-time special effect andsound that may be added to an image or a video.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, AR logos or emblems,and similar terms that refer to modifications that may be applied toimage data (e.g., videos or images). This includes real-timemodifications, which modify an image as it is captured using devicesensors (e.g., one or multiple cameras) of a client device 102 and thendisplayed on a screen of the client device 102 with the modifications.This also includes modifications to stored content, such as video clipsin a gallery that may be modified. For example, in a client device 102with access to multiple augmented reality content items, a user can usea single video clip with multiple augmented reality content items to seehow the different augmented reality content items will modify the storedclip. For example, multiple augmented reality content items that applydifferent pseudorandom movement models can be applied to the samecontent by selecting different augmented reality content items for thecontent. Similarly, real-time video capture may be used with anillustrated modification to show how video images currently beingcaptured by sensors of a client device 102 would modify the captureddata. Such data may simply be displayed on the screen and not stored inmemory, or the content captured by the device sensors may be recordedand stored in memory with or without the modifications (or both). Insome systems, a preview feature can show how different augmented realitycontent items will look within different windows in a display at thesame time. This can, for example, enable multiple windows with differentpseudorandom animations to be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousexamples, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human’s face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of an object’s elements, characteristic points for each element ofan object are calculated (e.g., using an Active Shape Model (ASM) orother known methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification, properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various examples, any combination ofsuch modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

Other methods and algorithms suitable for face detection can be used.For example, in some examples, features are located using a landmark,which represents a distinguishable point present in most of the imagesunder consideration. For facial landmarks, for example, the location ofthe left eye pupil may be used. If an initial landmark is notidentifiable (e.g., if a person has an eyepatch), secondary landmarksmay be used. Such landmark identification procedures may be used for anysuch objects. In some examples, a set of landmarks forms a shape. Shapescan be represented as vectors using the coordinates of the points in theshape. One shape is aligned to another with a similarity transform(allowing translation, scaling, and rotation) that minimizes the averageEuclidean distance between shape points. The mean shape is the mean ofthe aligned training shapes.

In some examples, a search is started for landmarks from the mean shapealigned to the position and size of the face determined by a global facedetector. Such a search then repeats the steps of suggesting a tentativeshape by adjusting the locations of shape points by template matching ofthe image texture around each point and then conforming the tentativeshape to a global shape model until convergence occurs. In some systems,individual template matches are unreliable, and the shape model poolsthe results of the weak template matches to form a stronger overallclassifier. The entire search is repeated at each level in an imagepyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user’s face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransformation system initiates a process to convert the image of theuser to reflect the selected modification icon (e.g., generate a smilingface on the user). A modified image or video stream may be presented ina graphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine-taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the face beingmodified by the transformation system and store it for later viewing orbrowse to other areas of the imaging application. Where multiple facesare modified by the transformation system, the user may toggle themodification on or off globally by tapping or selecting a single facemodified and displayed within a graphical user interface. In someexamples, individual faces, among a group of multiple faces, may beindividually modified, or such modifications may be individually toggledby tapping or selecting the individual face or a series of individualfaces displayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Trained machine learning technique(s) 307 stores parameters that havebeen trained during training of the AR fashion control system 224. Forexample, trained machine learning techniques 307 stores the trainedparameters of one or more neural network machine learning techniques.

Segmentation training images 309 stores a plurality of images that eachdepict one or more users wearing different garments. The plurality ofimages stored in the segmentation training images 309 includes variousdepictions of one or more users wearing different garments together withsegmentations of the garments that indicate which pixels in the imagescorrespond to the garments and which pixels correspond to a backgroundor a user’s body parts in the images. Namely the segmentations providethe borders of the garments depicted in the images. These segmentationtraining images 309 are used by the AR fashion control system 224 totrain the machine learning technique used to generate a segmentation ofone or more garments depicted in a received RGB monocular image. In somecases, the segmentation training images 309 include ground truthskeletal key points of one or more bodies depicted in the respectivetraining monocular images to enhance segmentation performance on variousdistinguishing attributes (e.g., shoulder straps, collar or sleeves) ofthe garments. In some cases, the segmentation training images 309include a plurality of image resolutions of bodies depicted in theimages. The segmentation training images 309 can include labeled andunlabeled image and video data. The segmentation training images 309 caninclude a depiction of a whole body of a particular user, an image thatlacks a depiction of any user (e.g., a negative image), a depiction of aplurality of users wearing different garments, and depictions of userswearing garments at different distances from an image capture device.

The segmentation training images 309 can also store various depictionsof different facial expressions of different users. In this case, thesegmentation training images 309 are used by the AR fashion controlsystem 224 to train the machine learning technique to identify a facialexpression of a person depicted in an image or video. The segmentationtraining images 309 include ground truth emotions or moods associatedwith a depicted facial expression that is used to train the machinelearning technique.

The segmentation training images 309 can also store various depictionsof different makeup features applied to faces of different users. Inthis case, the segmentation training images 309 are used by the ARfashion control system 224 to train the machine learning technique toestimate a makeup classification of real or AR makeup applied to a faceof a person depicted in an image or video. The segmentation trainingimages 309 include ground truth makeup classifications associated with adepicted makeup that is used to train the machine learning technique.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   message identifier 402: a unique identifier that identifies the    message 400.-   message text payload 404: text, to be generated by a user via a user    interface of the client device 102, and that is included in the    message 400.-   message image payload 406: image data, captured by a camera    component of a client device 102 or retrieved from a memory    component of a client device 102, and that is included in the    message 400. Image data for a sent or received message 400 may be    stored in the image table 312.-   message video payload 408: video data, captured by a camera    component or retrieved from a memory component of the client device    102, and that is included in the message 400. Video data for a sent    or received message 400 may be stored in the video table 304.-   message audio payload 410: audio data, captured by a microphone or    retrieved from a memory component of the client device 102, and that    is included in the message 400.-   message augmentation data 412: augmentation data (e.g., filters,    stickers, or other annotations or enhancements) that represents    augmentations to be applied to message image payload 406, message    video payload 408, or message audio payload 410 of the message 400.    Augmentation data for a sent or received message 400 may be stored    in the augmentation table 310.-   message duration parameter 414: parameter value indicating, in    seconds, the amount of time for which content of the message (e.g.,    the message image payload 406, message video payload 408, message    audio payload 410) is to be presented or made accessible to a user    via the messaging client 104.-   message geolocation parameter 416: geolocation data (e.g.,    latitudinal and longitudinal coordinates) associated with the    content payload of the message. Multiple message geolocation    parameter 416 values may be included in the payload, each of these    parameter values being associated with respect to content items    included in the content (e.g., a specific image within the message    image payload 406, or a specific video in the message video payload    408).-   message story identifier 418: identifier values identifying one or    more content collections (e.g., “stories” identified in the story    table 314) with which a particular content item in the message image    payload 406 of the message 400 is associated. For example, multiple    images within the message image payload 406 may each be associated    with multiple content collections using identifier values.-   message tag 420: each message 400 may be tagged with multiple tags,    each of which is indicative of the subject matter of content    included in the message payload. For example, where a particular    image included in the message image payload 406 depicts an animal    (e.g., a lion), a tag value may be included within the message tag    420 that is indicative of the relevant animal. Tag values may be    generated manually, based on user input, or may be automatically    generated using, for example, image recognition.-   message sender identifier 422: an identifier (e.g., a messaging    system identifier, email address, or device identifier) indicative    of a user of the client device 102 on which the message 400 was    generated and from which the message 400 was sent.-   message receiver identifier 424: an identifier (e.g., a messaging    system identifier, email address, or device identifier) indicative    of a user of the client device 102 to which the message 400 is    addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

Ar Fashion Control System

FIG. 5 is a block diagram showing an example AR fashion control system224, according to example examples. AR fashion control system 224includes a set of components 510 that operate on a set of input data(e.g., a monocular image 501 depicting a real body of a user wearing ashirt and segmentation training image data 502). The set of input datais obtained from segmentation training images 309 stored in database(s)(FIG. 3 ) during the training phases and is obtained from an RGB cameraof a client device 102 when an AR/VR application is being used, such asby a messaging client 104. AR fashion control system 224 includes amachine learning technique module 512, a skeletal key-points module 511,a upper garment segmentation module 514, an image modification module518, an AR effect selection module 519, a facial expressionidentification module 530, a 3D body tracking module 513, a whole-bodysegmentation module 515, and an image display module 520.

During training, the AR fashion control system 224 receives a giventraining image (e.g., monocular image 501 depicting a real body of auser wearing a garment, such as an image of a user wearing as a shirt(short sleeve, t-shirt, or long sleeve), jacket, tank top, sweater, andso forth, a lower body garment, such as pants or a skirt, a whole bodygarment, such as a dress or overcoat, or any suitable combinationthereof or depicting multiple users simultaneously wearing respectivecombinations of upper body garments, lower body garments or whole bodygarments) from segmentation training image data 502. The AR fashioncontrol system 224 applies one or more machine learning techniques usingthe machine learning technique module 512 on the given training image.The machine learning technique module 512 extracts one or more featuresfrom the given training image to estimate a segmentation of thegarment(s) worn by the user(s) depicted in the image. For example, themachine learning technique module 512 obtains the given training imagedepicting a user wearing a shirt. The machine learning technique module512 extracts features from the image and segments or specifies whichpixels in the image correspond to the shirt worn by the user and whichpixels correspond to a background or correspond to parts of the user’sbody. Namely, the segmentation output by the machine learning techniquemodule 512 identifies borders of a garment (e.g., the shirt) worn by theuser in the given training image.

The machine learning technique module 512 retrieves garment segmentationinformation associated with the given training image. The machinelearning technique module 512 compares the estimated segmentation (thatcan include an identification of multiple garments worn by respectiveusers in the image in case there exist multiple users in the image) withthe ground truth garment segmentation provided as part of thesegmentation training image data 502. Based on a difference threshold ordeviation of the comparison, the machine learning technique module 512updates one or more coefficients or parameters and obtains one or moreadditional segmentation training images. After a specified number ofepochs or batches of training images have been processed and/or when thedifference threshold or deviation reaches a specified value, the machinelearning technique module 512 completes training and the parameters andcoefficients of the machine learning technique module 512 are stored inthe trained machine learning technique(s) 307.

In some examples, the machine learning technique module 512 implementsmultiple segmentation models of the machine learning technique. Eachsegmentation model of the machine learning technique module 512 may betrained on a different set of training images associated with a specificresolution. Namely, one of the segmentation models can be trained toestimate a garment segmentation for images having a first resolution (ora first range of resolutions). A second of the segmentation models canbe trained to estimate a garment segmentation for images having a secondresolution (or a second range of resolutions different from the firstrange of resolutions). In this way, different complexities of themachine learning technique module 512 can be trained and stored. When agiven device having certain capabilities uses the AR/VR application, acorresponding one of the various garment segmentation models can beprovided to perform the garment segmentation that matches thecapabilities of the given device. In some cases, multiple garmentsegmentation models of each of the machine leaning techniquesimplemented by the AR fashion control system 224 can be provided eachconfigured to operate with a different level of complexity. Theappropriate segmentation model(s) with the appropriate level ofcomplexity can then be provided to a client device 102 for segmentinggarments depicted in one or more images.

In some examples, during training, the machine learning technique module512 receives 2D skeletal joint information from a skeletal key-pointsmodule 511. The skeletal key-points module 511 tracks skeletal keypoints of a user depicted in a given training image (e.g., head joint,shoulder joints, hip joints, leg joints, and so forth) and provides the2D or 3D coordinates of the skeletal key points. This information isused by the machine learning technique module 512 to identifydistinguishing attributes of the garment depicted in the training image.

The garment segmentation generated by the machine learning techniquemodule 512 is provided to the upper garment segmentation module 514. Theupper garment segmentation module 514 can determine that the elbow jointoutput by the skeletal key-points module 511 is at a position that iswithin a threshold distance away from a given edge of the border of theshirt garment segmentation. In response, the upper garment segmentationmodule 514 can determine that the garment corresponds to a t-shirt orshort sleeve shirt and that the given edge corresponds to a sleeve ofthe shirt. In such circumstances, the upper garment segmentation module514 can adjust weights of the parameters or the loss function used toupdate parameters of the machine learning technique module 512 toimprove segmentation of upper body garments, such as shirts. Morespecifically, the upper garment segmentation module 514 can determinethat a given distinguishing attribute is present in the garmentsegmentation that is generated based on a comparison of skeletal jointpositions to borders of the garment segmentation. In such circumstances,the upper garment segmentation module 514 adjusts the loss function orweights used to update the parameters of the machine learning techniquemodule 512 for the training image depicting the garment with thedistinguishing attribute. Similarly, the upper garment segmentationmodule 514 can adjust the loss or the parameter weights based on adifference between the garment segmentation and the pixels correspondingto the background of the image.

The upper garment segmentation module 514 is used to track a 2D or 3Dposition of the segmented shirt in subsequent frames of a video. Thisenables one or more AR elements to be displayed on the shirt and bemaintained at their respective positions on the shirt as the position ofthe shirt moves around the screen. In this way, the upper garmentsegmentation module 514 can determine and track which portions of theshirt are currently shown in the image that depicts the user and toselectively adjust the corresponding AR elements that are displayed. Forexample, a given AR element can be displayed on a left sleeve of theshirt in a first frame of the video. The upper garment segmentationmodule 514 can determine that in a second frame of the video the userhas turned left, meaning that the left sleeve no longer appears in thesecond frame. In response, the upper garment segmentation module 514 canomit entirely or a portion of the given AR element that was displayed onthe left sleeve of the shirt.

After training, AR fashion control system 224 receives an input image501 (e.g., monocular image depicting a user wearing a garment ormultiple users wearing respective garments) as a single RGB image from aclient device 102. The AR fashion control system 224 applies the trainedmachine learning technique module 512 to the received input image 501 toextract one or more features of the image to generate a segmentation ofthe garment or garments depicted in the image 501. This segmentation isprovided to the upper garment segmentation module 514 to track the 2D or3D position of the shirt in the current frame of the video and insubsequent frames.

FIG. 6 is a diagrammatic representation of outputs of the AR fashioncontrol system 224, in accordance with some examples. Specifically, FIG.6 shows a garment segmentation 600 generated by the upper garmentsegmentation module 514. In one example, the upper garment segmentationmodule 514 generates a first garment segmentation 612 representing pixellocations of a shirt worn by a user. In another example, the uppergarment segmentation module 514 generates a second garment segmentationrepresenting pixel locations of a short sleeve shirt worn by a user. Inanother example, the upper garment segmentation module 514 generates athird garment segmentation representing pixel locations of a jacket wornby a user.

Referring back to FIG. 5 , AR effect selection module 519 receives froma facial expression identification module 530 an indication (e.g., amood or emotion) associated with a facial expression performed by a useror person depicted in an image or video captured by a client device 102.Based on the facial expression performed by the user, the AR effectselection module 519 selects and applies one or more AR elements to theshirt segmentation received from the upper garment segmentation module514. This shirt segmentation combined with the one or more AR elementsis provided to the image modification module 518 to render an image orvideo that depicts the user wearing a shirt with the one or more ARelements.

For example, a user of the AR/VR application may be presented with anoption to perform facial expressions to control display of AR elementson a shirt worn by the user. In response to receiving a user selectionof the option, a camera (e.g., front-facing or rear-facing camera) isactivated to begin capturing an image or video of the user wearing ashirt. The image or video depicting the user wearing the shirt isprovided to the facial expression identification module 530 to detect afacial expression performed by the user. This facial expression is usedby the AR effect selection module 519 to select between variousapplications/modifications of AR elements displayed on the shirt worn bythe user. FIGS. 7A, 7B, 8, and 9 show illustrative outputs of one ormore of the visual effects that can be selected by the AR effectselection module 519 based on the facial expressions detected as beingperformed by the user or person depicted in the image or video receivedfrom the client device 102.

The image modification module 518 can adjust the image captured by thecamera based on the AR effect selected by the visual effect selectionmodule 519. The image modification module 518 adjusts the way in whichthe garment worn by the user is/are presented in an image, such as bychanging the color or occlusion pattern of the garment(s) worn by theuser based on the garment segmentation and applying one or more ARelements (AR graphical elements) to the garment worn by the userdepicted in the image or video. Image display module 520 combines theadjustments made by the image modification module 518 into the receivedmonocular image depicting the user’s body. The image is provided by theimage display module 520 to the client device 102 and can then be sentto another user or stored for later access and display.

In some examples, the image modification module 518 and/or the facialexpression identification module 530 receive 3D body trackinginformation representing the 3D positions of the user depicted in theimage from the 3D body tracking module 513. The 3D body tracking module513 generates the 3D body tracking information by processing the image501 and the monocular video 503 using additional machine learningtechniques. The image modification module 518 can also receive awhole-body segmentation representing which pixels in the imagecorrespond to the whole body of the user from another machine learningtechnique. The whole-body segmentation can be received from thewhole-body segmentation module 515. The whole-body segmentation module515 generates the whole body segmentation by processing the image 501using a machine learning technique.

The facial expression identification module 530 can implement one ormore machine learning techniques (e.g., neural networks) to estimate oridentify a mood or emotion associated with a facial expression performedby a user or person depicted in an image. Specifically, during training,the facial expression identification module 530 receives a giventraining image (e.g., monocular image 501 depicting a facial expressionperformed by a user) from segmentation training image data 502. Thefacial expression identification module 530 applies one or more machinelearning techniques to facial features of the person depicted in thegiven training image. The facial expression identification module 530extracts one or more facial features from the given training image toestimate a mood or emotion associated with the facial features of theperson depicted in the image. The facial expression identificationmodule 530 compares the estimated mood or emotion with the ground truthgarment mood or emotion provided as part of the segmentation trainingimage data 502. Based on a difference threshold or deviation of thecomparison, the facial expression identification module 530 updates oneor more coefficients or parameters and obtains one or more additionalsegmentation training images. After a specified number of epochs orbatches of training images have been processed and/or when thedifference threshold or deviation reaches a specified value, the facialexpression identification module 530 completes training and theparameters and coefficients of the facial expression identificationmodule 530 are stored in the trained machine learning technique(s) 307.

After being trained, the facial expression identification module 530receives an image depicting a person’s face. The facial expressionidentification module 530 processes facial features of the facialexpression of the person’s face to estimate a mood or emotion associatedwith the facial features. The estimated mood or emotion is provided tothe AR effect selection module 519 to apply one or more AR effects tothe image depicting the person’s face, such as by applying the one ormore AR effects to a shirt worn by the person depicted in the image orvideo. In other implementations, the facial expression identificationmodule 530 obtains one or more facial features of the person’s face andsearches a database of facial features to identify facial features thatmatch the obtained facial features. The identified facial features inthe database can be associated with one or more moods or emotions. Thefacial expression identification module 530 then retrieves the mood oremotion associated with the identified facial features to provide to theAR effect selection module 519.

The image modification module 518 can control the display of virtual orAR elements based on the garment segmentation provided by the uppergarment segmentation module 514 and based on the 3D body trackingpositions of the user and the whole-body segmentation of the user.Specifically, the image modification module 518 can control theocclusion pattern of an AR element relative to the real-world garmentcorresponding to the garment segmentation. Namely, the imagemodification module 518 determines which portion of the AR element toocclude with pixels of the real-world garment and/or also determineswhich portion of the real-world garment pixels to occlude with pixels ofthe AR element.

In one example, the AR effect selection module 519 may implement one ormore machine learning techniques (e.g., neural networks) to estimate oridentify a makeup classification associated with makeup features appliedto a face of a user or person depicted in an image. Specifically, duringtraining, the AR effect selection module 519 receives a given trainingimage (e.g., monocular image 501 depicting makeup applied to a face of auser) from segmentation training image data 502. The AR effect selectionmodule 519 applies one or more machine learning techniques to makeupfeatures of the makeup applied to the person depicted in the giventraining image. The AR effect selection module 519 extracts one or moremakeup features from the given training image to estimate a makeupclassification associated with the makeup features of the makeup appliedto the face of the person depicted in the image. The AR effect selectionmodule 519 compares the estimated makeup classification (e.g., goth,animal style, and so forth) with the ground truth garment makeupclassification provided as part of the segmentation training image data502. Based on a difference threshold or deviation of the comparison, theAR effect selection module 519 updates one or more coefficients orparameters and obtains one or more additional segmentation trainingimages. After a specified number of epochs or batches of training imageshave been processed and/or when the difference threshold or deviationreaches a specified value, the AR effect selection module 519 completestraining and the parameters and coefficients of the AR effect selectionmodule 519 are stored in the trained machine learning technique(s) 307.

After being trained, the AR effect selection module 519 receives animage depicting makeup applied to a person’s face. The AR effectselection module 519 processes makeup features of the makeup applied tothe face the person to estimate a makeup classification associated withthe makeup features. The estimated makeup classification is used toapply one or more AR effects to the image depicting the person’s face,such as by applying the one or more AR effects to a shirt worn by theperson depicted in the image or video. In other implementations, the AReffect selection module 519 obtains one or more makeup features andsearches a database of makeup features to identify makeup features thatmatch the obtained facial features. The identified makeup features inthe database can be associated with one or more makeup classifications.The AR effect selection module 519 then retrieves the makeupclassification associated with the identified makeup features to selectand generate the corresponding AR effects on the shirt worn by theperson depicted in the image.

In one example, as shown in FIG. 7A, the AR effect selection module 519can apply one or more AR effects 730 to a shirt 710 worn by a user 720depicted in an image 700 captured by a client device 102 based on afacial expression of the user 720. For example, the AR effect selectionmodule 519 can apply a shirt color AR effect to modify a color of theshirt 710 based on the mood or emotion associated with the facialexpression of the user 720. The shirt color AR effect can be adjusted inreal time as the facial expression of the user 720 changes in subsequentframes of a video. In another example, the AR effect selection module519 can apply a background color AR effect to modify a color of abackground 740 based on the mood or emotion associated with the facialexpression of the user 720. The background color AR effect can beadjusted in real time as the facial expression of the user 720 changesin subsequent frames of a video.

In another example, the AR effect selection module 519 can generate animage or video, as the AR effects 730, for display on the shirt 710 wornby the user 720 depicted in the image 700. The image or video can berendered on the shirt 710 worn by the user 720 in response to detectinga particular facial expression; or an image or video that is playing ordisplayed in the background 740 can be adjusted based on changes tofacial expressions of the person depicted in the image or video. Forexample, the AR effect selection module 519 can receive indication of aparticular mood or emotion associated with a facial expression of theuser 720 depicted in the image 700. The AR effect selection module 519can search a database of words to identify one or more words that areassociated with the facial expression (e.g., the emotion or mood of thefacial expression). The messaging client 104 can select a subset of theidentified words, such as based on a rank, popularity, user profile,randomness, uniqueness, or any combination thereof. The AR effectselection module 519 can then search for an image or video (e.g., anavatar or emoji) associated with the selected subset of the identifiedwords and add the image or video as the AR effects 730 to the shirt 710worn by the user 720 in the image 700.

In one implementation, the AR effect selection module 519 can controlplayback of a video or apply a media item adjustment parameter to animage or video presented on the image 700 based on the facial expressionof the user 720. For example, the AR effect selection module 519 canadjust the aspect ratio, the brightness, pause, play, fast-forward orrewind an image or video that is displayed in the image 700 based on thefacial expression of the user 720. Any one or multiple media itemadjustment parameters can be applied in response to a particular facialexpression being detected. As different facial expressions are detected,the AR effect selection module 519 selects and applies different mediaitem adjustment parameters.

As one example, the AR effect selection module 519 can adjust a fashionitem pattern, type or style based on makeup applied to a face of aperson depicted in an image or video. For example, the AR effectselection module 519 can process one or more facial features of the user720 depicted in the image 700. The AR effect selection module 519 candetermine a style of the makeup on the face of the user, as discussedabove. The AR effect selection module 519 can search a database offashion item patterns, types or styles to identify a fashion itempattern, type, or style associated with the style of makeup. The AReffect selection module 519 can then select a given one of the fashionitem patterns, styles, or types to generate the AR effect 730 associatedwith the selected fashion item pattern, style, or type. The AR effectselection module 519 applies the AR effect 730 to the fashion item wornby the user 720 depicted in the image 700. In this way, the AR effectselection module 519 can adjust the fashion item or fashion items wornby the user 720 depicted in the image 700 to match a makeup styleassociated with makeup on the face of the user 720, such as by applyingone or more AR effects 730 to one or more articles of clothing worn bythe user 720.

In one example, the AR effect selection module 519 can generate, as theAR effects 730, text for display on the shirt worn by the user 720depicted in the image 700. The text can be rendered on the shirt 710worn by the user 720 in response to detecting a particular facialexpression. For example, the AR effect selection module 519 can receiveindication of a particular facial expression of the user 720 from thefacial expression identification module 530. The AR effect selectionmodule 519 can search a database of words to identify one or more wordsthat are associated with the facial expression (e.g., the emotion ormood of the facial expression). The AR effect selection module 519 canselect a subset of the identified words, such as based on a rank,popularity, user profile, randomness, uniqueness, or any combinationthereof. The AR effect selection module 519 can then add an AR effect730 that includes text to the shirt 710 worn by the user 720 in theimage 700. The AR effect selection module 519 can include, in the textof the AR effect 730, the selected subset of the identified words.

In another example, as shown in FIG. 7B, the AR effect selection module519 can replace words of existing text with other words based on changesto facial expressions of the user 722 depicted in the image or video701. For example, the AR effect selection module 519 can detect a phrase731 written physically on the shirt 712 worn by the user 722 depicted inthe image or video 701. The AR effect selection module 519 can performword recognition to the phrase to identify a word associated with anadjective that describes an emotion or mood. In response to identifyingthe word associated with the adjective, the AR effect selection module519 can generate an augmented reality version of the identified word andinclude text in the augmented reality version that represents the facialexpression. The AR effect selection module 519 can replace a portion ofthe phrase 731 (e.g., a particular word) with the augmented realityversion of the word, such as by overlaying the augmented reality versionof the word on top of the physical word in the phrase 731 that is on theshirt 712 worn by the user 722 depicted in the image or video 701.

As another example, the AR effect selection module 519 can adjust anexpression of an augmented reality avatar or emoji depicted on a shirtworn by a person in the image or video to match the facial expression ofthe person depicted in the image or video. For example, as shown in FIG.8 , the AR effect selection module 519 can detect a face 826 of a persondepicted in the image or video 800. In response to detecting the face826, the AR effect selection module 519 can receive an indication of anemotion or mood associated with a facial expression of the detected face826. The AR effect selection module 519 can search a database of avataror emoji expressions that are associated with the facial expression(e.g., the emotion or mood of the facial expression). The AR effectselection module 519 can then adjust a facial expression of an avatar oremoji 840 displayed on a shirt 810 worn by the person depicted in theimage or video 800 to match or correspond to the facial expression ofthe person depicted in the image or video 800. The facial expression ofthe avatar or emoji 840 can be updated in real time based on detectingchanges to the facial expression of the detected face 826. In oneimplementation, the AR effect selection module 519 can present apurchase option 850 together with the depiction of the shirt and avataror emoji 840 on top of the shirt 810. In response to receiving a userselection of the purchase option 850, the AR effect selection module 519can transmit an image of the shirt 810 with the avatar or emoji 840having the matched facial expression to a vendor that manufacturesshirts. The AR effect selection module 519 can instruct the vendor tocreate a physical shirt in a size associated with the person depicted inthe image or video 800 that resembles the shirt worn by the personaugmented with the avatar or emoji 840. The manufactured shirt is thenshipped to the home of the person.

As another example, as shown in FIG. 9 , the AR effect selection module519 can determine that the style of makeup 940 applied to a face 930 ofa user is associated with a particular animal (e.g., a lion or zebra).In such cases, the AR effect selection module 519 obtains acharacteristic of the animal (e.g., zebra or lion stripes) and appliesaugmented reality zebra or lion stripes 920 to a shirt 910 worn by theuser depicted in the image or video 900. In an implementation, the AReffect selection module 519 adds an augmented reality headwear 950 tothe person’s head (e.g., to represent horns) of the animal. As anotherexample, the AR effect selection module 519 can determine that themakeup type of the makeup 940 applied to the face 930 is associated witha goth style and, in response, the AR effect selection module 519modifies a color, pattern, or type of clothing worn by the user depictedin the image or video 900. The modification can be performed byoccluding or overlaying a portion of the physical clothing worn by theuser with augmented reality clothing.

The AR effect selection module 519 can display, on the shirt 910depicted in the image or video 900, an augmented reality version 922 ofan augmented reality facial effect applied to the face 930 of the userdepicted in the image or video 900. Specifically, the messaging client104 can detect an augmented reality facial effect applied to the face930 of the user depicted in the image (e.g., such as augmented realitymakeup). The AR effect selection module 519 can, in response, generatean augmented reality version 922 of the augmented reality facial effectby copying a style, pattern, pose, outline, and color of the face 930 ofthe user to which the augmented reality facial effect has been appliedinto an augmented reality version 922. The AR effect selection module519 can then overlay the augmented reality version 922 of the facialeffect on top of a specified portion of the shirt 910 worn by the userdepicted in the image or video 900. In some cases, the AR effectselection module 519 can present a purchase option 960 together with thedepiction of the shirt 910 and augmented reality version 922 of thefacial effect on top of the shirt 910. In response to receiving a userselection of the purchase option 960, the AR effect selection module 519can transmit an image of the shirt 910 with the augmented realityversion 922 of the facial effect to a vendor that manufactures shirts.The AR effect selection module 519 can instruct the vendor to create aphysical shirt in a size associated with the user that resembles theshirt 910 worn by the user augmented with the augmented reality version922 of the facial effect. The manufactured shirt is then shipped to thehome of the user.

The image modification module 518 determines which subset of pixels ofthe real-world shirt overlap a subset of pixels of the music ARelement(s). If the occlusion pattern indicates that the AR element(s)occludes the real-world shirt garment, the image modification module 518replaces the subset of pixels of the real-world shirt with the subset ofpixels of the AR element(s).

FIG. 10 is a flowchart of a process 1000 performed by the AR fashioncontrol system 224, in accordance with some example examples. Althoughthe flowchart can describe the operations as a sequential process, manyof the operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed. A process may correspondto a method, a procedure, and the like. The steps of methods may beperformed in whole or in part, may be performed in conjunction with someor all of the steps in other methods, and may be performed by any numberof different systems or any portion thereof, such as a processorincluded in any of the systems.

At operation 1001, the AR fashion control system 224 (e.g., a clientdevice 102 or a server) receives an image that includes a depiction of aperson wearing a fashion item (e.g., a shirt), as discussed above.

At operation 1002, the AR fashion control system 224 generates asegmentation of the shirt worn by the person depicted in the image, asdiscussed above.

At operation 1003, the AR fashion control system 224 identifies a facialexpression of the person depicted in the image, as discussed above.

At operation 1004, the AR fashion control system 224, based on theidentified facial expression, applies one or more augmented realityelements to the shirt worn by the person based on the segmentation ofthe shirt worn by the person, as discussed above.

Machine Architecture

FIG. 11 is a diagrammatic representation of the machine 1100 withinwhich instructions 1108 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1100to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1108 may cause the machine 1100to execute any one or more of the methods described herein. Theinstructions 1108 transform the general, non-programmed machine 1100into a particular machine 1100 programmed to carry out the described andillustrated functions in the manner described. The machine 1100 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1100 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1100 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1108, sequentially or otherwise,that specify actions to be taken by the machine 1100. Further, whileonly a single machine 1100 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1108 to perform any one or more of themethodologies discussed herein. The machine 1100, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1100 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1100 may include processors 1102, memory 1104, andinput/output (I/O) components 1138, which may be configured tocommunicate with each other via a bus 1140. In an example, theprocessors 1102 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1106 and a processor 1110 that execute the instructions 1108.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 11 shows multiple processors 1102, the machine 1100 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 1104 includes a main memory 1112, a static memory 1114, and astorage unit 1116, all accessible to the processors 1102 via the bus1140. The main memory 1104, the static memory 1114, and the storage unit1116 store the instructions 1108 embodying any one or more of themethodologies or functions described herein. The instructions 1108 mayalso reside, completely or partially, within the main memory 1112,within the static memory 1114, within machine-readable medium 1118within the storage unit 1116, within at least one of the processors 1102(e.g., within the processor’s cache memory), or any suitable combinationthereof, during execution thereof by the machine 1100.

The I/O components 1138 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1138 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1138 mayinclude many other components that are not shown in FIG. 11 . In variousexamples, the I/O components 1138 may include user output components1124 and user input components 1126. The user output components 1124 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1126 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1138 may include biometriccomponents 1128, motion components 1130, environmental components 1132,or position components 1134, among a wide array of other components. Forexample, the biometric components 1128 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1130 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 1132 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 1134 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1138 further include communication components 1136operable to couple the machine 1100 to a network 1120 or devices 1122via respective coupling or connections. For example, the communicationcomponents 1136 may include a network interface component or anothersuitable device to interface with the network 1120. In further examples,the communication components 1136 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth^(®)components (e.g., Bluetooth^(®) Low Energy), Wi-Fi^(®) components, andother communication components to provide communication via othermodalities. The devices 1122 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1136 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1136 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1136, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1112, static memory 1114, andmemory of the processors 1102) and storage unit 1116 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1108), when executedby processors 1102, cause various operations to implement the disclosedexamples.

The instructions 1108 may be transmitted or received over the network1120, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1136) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 1108may be transmitted or received using a transmission medium via acoupling (e.g., a peer-to-peer coupling) to the devices 1122.

Software Architecture

FIG. 12 is a block diagram 1200 illustrating a software architecture1204, which can be installed on any one or more of the devices describedherein. The software architecture 1204 is supported by hardware such asa machine 1202 that includes processors 1220, memory 1226, and I/Ocomponents 1238. In this example, the software architecture 1204 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1204 includes layerssuch as an operating system 1212, libraries 1210, frameworks 1208, andapplications 1206. Operationally, the applications 1206 invoke API calls1250 through the software stack and receive messages 1252 in response tothe API calls 1250.

The operating system 1212 manages hardware resources and provides commonservices. The operating system 1212 includes, for example, a kernel1214, services 1216, and drivers 1222. The kernel 1214 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1214 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1216 canprovide other common services for the other software layers. The drivers1222 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1222 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1210 provide a common low-level infrastructure used byapplications 1206. The libraries 1210 can include system libraries 1218(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1210 can include APIlibraries 1224 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1210 can also include a widevariety of other libraries 1228 to provide many other APIs to theapplications 1206.

The frameworks 1208 provide a common high-level infrastructure that isused by the applications 1206. For example, the frameworks 1208 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1208 canprovide a broad spectrum of other APIs that can be used by theapplications 1206, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1206 may include a home application1236, a contacts application 1230, a browser application 1232, a bookreader application 1234, a location application 1242, a mediaapplication 1244, a messaging application 1246, a game application 1248,and a broad assortment of other applications such as a externalapplication 1240. The applications 1206 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1206, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1240 (e.g., an application developed using the ANDROID™ or IOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the external application 1240can invoke the API calls 1250 provided by the operating system 1212 tofacilitate functionality described herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1xRTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various examples, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application specificintegrated circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component”(or"hardware-implemented component") should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein.

Considering examples in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inexamples in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1102 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some examples, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed examples withoutdeparting from the scope of the present disclosure. These and otherchanges or modifications are intended to be included within the scope ofthe present disclosure, as expressed in the following claims.

What is claimed is_(:)
 1. A method comprising: receiving, by one or moreprocessors of a client device, an image that includes a depiction of aperson wearing a fashion item; generating, by the one or moreprocessors, a segmentation of the fashion item worn by the persondepicted in the image; identifying a facial expression of the persondepicted in the image; and in response to identifying the facialexpression, applying one or more augmented reality elements to thefashion item worn by the person based on the segmentation of the fashionitem worn by the person.
 2. The method of claim 1, wherein the fashionitem comprises a shirt, and wherein applying the one or more augmentedreality elements to the shirt worn by the person further comprises:generating text for display on the shirt; selecting one or more wordsfor the text based on the facial expression of the person; and adjustingthe generated text to include the selected one or more words.
 3. Themethod of claim 1, further comprising: adding text to the fashion itemworn by the person depicted in the image in response to identifying thefacial expression.
 4. The method of claim 3, wherein adding the textcomprises: selecting one or more words associated with the facialexpression; and generating the text to include the selected one or morewords for display on the fashion item.
 5. The method of claim 1, furthercomprising: adding an image or video to the fashion item worn by theperson depicted in the image in response to identifying the facialexpression.
 6. The method of claim 5, wherein adding the image or videofurther comprises: selecting an image or video adjustment parameterassociated with the facial expression; and adjusting the image or videobased on the selected image or video adjustment parameter.
 7. The methodof claim 6, wherein the image or video adjustment parameter comprises anaspect ratio modification, a brightness modification, or a playbackselection.
 8. The method of claim 1, further comprising: adding anavatar or emoji to the fashion item worn by the person depicted in theimage in response to identifying the facial expression; and adjusting anexpression of the avatar or emoji based on the facial expression of theperson.
 9. The method of claim 1, further comprising: processing one ormore facial features of the person depicted in the image to detectmakeup on a face of the person; determining a style of the makeup on theface of the person; and selecting, as the one or more augmented realityelements, a fashion item pattern or style corresponding to the style ofthe makeup on the face of the person.
 10. The method of claim 9, furthercomprising: obtaining makeup features of the makeup on the face of theperson; and applying a machine learning technique to the makeup featuresto estimate a classification of the makeup on the face of the person,wherein the fashion item pattern or style is selected based on theclassification of the makeup.
 11. The method of claim 1, furthercomprising: activating an augmented reality makeup experience; applyingaugmented reality makeup to a face of the person depicted in the image;determining a style of the augmented reality makeup applied to the faceof the person; and selecting, as the one or more augmented realityelements, a fashion item pattern, type or style corresponding to thestyle of the augmented reality makeup applied to the face of the person.12. The method of claim 11, wherein the style of the augmented realitymakeup is associated with an animal, and wherein the fashion itempattern, type or style comprises a characteristic of the animalassociated with the augmented reality makeup applied to the face of theperson.
 13. The method of claim 12, wherein the animal comprises a lionor zebra, and wherein the characteristic comprises lion or zebrastripes.
 14. The method of claim 11, wherein the fashion item typecomprises headwear that is added in response to determining the style ofthe augmented reality makeup.
 15. The method of claim 11, wherein thefashion item style comprises goth style that is applied to the fashionitem or another garment worn by the person depicted in the image inresponse to determining the style of the augmented reality makeup. 16.The method of claim 1, further comprising: detecting an augmentedreality facial effect applied to a face of the person depicted in theimage; generating, as the one or more augmented reality elements, anaugmented reality version of the augmented reality facial effect; anddisplaying the augmented reality version of the augmented reality facialeffect on the fashion item worn by the person depicted in the image. 17.The method of claim 16, further comprising generating a purchase optionfor display together with the augmented reality version of the augmentedreality facial effect on the fashion item worn by the person depicted inthe image, wherein in response to receiving a selection of the purchaseoption, a physical product comprising a depiction of the fashion itemworn by the person and the augmented reality version of the augmentedreality facial effect is customized and created.
 18. A systemcomprising: a processor of a client device; and a memory componenthaving instructions stored thereon, when executed by the processor,causes the processor to perform operations comprising: receiving animage that includes a depiction of a person wearing a fashion item;generating a segmentation of the fashion item worn by the persondepicted in the image; identifying a facial expression of the persondepicted in the image; and in response to identifying the facialexpression, applying one or more augmented reality elements to thefashion item worn by the person based on the segmentation of the fashionitem worn by the person.
 19. The system of claim 18, the operationsfurther comprising: generating text for display on the fashion item;selecting one or more words for the text based on the facial expressionof the person; and adjusting the generated text to include the selectedone or more words.
 20. A non-transitory computer-readable storage mediumhaving stored thereon instructions that, when executed by a processor ofa client device, cause the processor to perform operations comprising:receiving an image that includes a depiction of a person wearing afashion item; generating a segmentation of the fashion item worn by theperson depicted in the image; identifying a facial expression of theperson depicted in the image; and in response to identifying the facialexpression, applying one or more augmented reality elements to thefashion item worn by the person based on the segmentation of the fashionitem worn by the person.